3D point cloud simplification based on the clustering algorithm and introducing the Shannon’s entropy

2021 ◽  
Author(s):  
Abdelaaziz Mahdaoui ◽  
El Hassan SBAI
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Shannon’S Entropy ◽  
3D Point Cloud ◽  
Shannon's Entropy ◽  
Point Cloud Simplification

scholarly journals 3D Point Cloud Simplification Based on k-Nearest Neighbor and Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Abdelaaziz Mahdaoui ◽  
El Hassan Sbai
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
3D Point Cloud ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
New Approach ◽  
Entropy Estimation ◽  
3D Objects ◽  
Point Cloud Simplification

While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor (k-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using k-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.

scholarly journals A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yang Yang ◽  
Ming Li ◽  
Xie Ma
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Cluster Center ◽  
Normal Vector ◽  
Fuzzy C Means ◽  
Feature Information ◽  
Point Cloud Simplification

To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.

3D Point Cloud Simplification Algorithm Based on Fuzzy Entropy Iteration

2013 ◽  
Vol 33 (8) ◽  
pp. 0815001
Author(s):  
陈璋雯 Chen Zhangwen ◽  
达飞鹏 Da Feipeng
Keyword(s):  
Point Cloud ◽  
Fuzzy Entropy ◽  
3D Point Cloud ◽  
Point Cloud Simplification

scholarly journals Estimation of shoulder width and neck girth based on 3D point cloud data

2021 ◽  
pp. 47-47
Author(s):  
Xin Lu ◽  
Panpan Guo ◽  
Guolian Liu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Young Female ◽  
3D Measurement ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Point Cloud Simplification

Three dimensional point cloud map in the anthropometry has attracted intensive attention due to the availability of fast and accurate laser scan devices. Inevitably, there is a data deviation between 3D measurement and manual tests. To address this problem, shoulder width and neck girth are accurately determined from 3D point cloud, the two-scale fractal is used for 3D point cloud simplification, and young female samples are used in our experiment to show the accuracy.

scholarly journals PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

2016 ◽  
Vol III-3 ◽  
pp. 201-208 ◽  
Author(s):  
Xiaohu Lu ◽  
Jian Yao ◽  
Jinge Tu ◽  
Kai Li ◽  
Li Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Input Parameter ◽  
Point Clouds ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Point Cloud Segmentation ◽  
Data Point

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.

scholarly journals PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

2016 ◽  
Vol III-3 ◽  
pp. 201-208 ◽  
Author(s):  
Xiaohu Lu ◽  
Jian Yao ◽  
Jinge Tu ◽  
Kai Li ◽  
Li Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Input Parameter ◽  
Point Clouds ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Point Cloud Segmentation ◽  
Data Point

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.

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